{
  "abstract": "Agentic workflows can coordinate modelling, but balancing predictive performance, measurement burden and reproducibility is unclear. We developed DXA Agent, an agentic workflow for dual-energy X-ray absorptiometry (DXA) outcomes integrating planning, feature-model refinement, tools, provenance and hypothesis-generating interpretation. Models were independently developed and tested in UK Biobank (5,318 participants) and the National Health and Nutrition Examination Survey (NHANES; 3,777 participants), using cost-efficient and no-limit strategies. Across 20 UK Biobank and three NHANES bone mineral density sites, cost-efficient models achieved lower RMSE and higher R{superscript 2} than the best conventional comparator, with median relative RMSE reductions of 10.9% and 9.9%, respectively. Classification was task dependent: UK Biobank osteoporosis averaged AUROC 0.839 and PR-AUC 0.182, whereas NHANES performance was comparable with conventional models. Higher-burden features did not consistently improve prediction. These retrospective, cohort-internal findings position DXA Agent as an inspectable, measurement-burden-aware research workflow requiring independent prospective validation.",
  "abstract_shown": true,
  "affiliations": [
    "Nation University of Singapore"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "S. Xiang"
    },
    {
      "name": "H. He"
    },
    {
      "name": "Z. Xie"
    },
    {
      "name": "C.-Y. Cheng"
    },
    {
      "name": "H. Li"
    },
    {
      "affs": [
        0
      ],
      "name": "D. Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8pb5yj38qbqa3.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.30.26361665",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.08.30.26361665v1.full",
  "id": "8pb5yj38qbqa3",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.08.30.26361665v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "2732|Bone health and osteoporosis research"
    ]
  ],
  "openalex": "W7204896906",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.08.30.26361665v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8pb5yj38qbqa3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:28Z",
      "native_id": "10.64898/2026.08.30.26361665",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.08.30.26361665v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Artificial Scientific Intelligence for Measurement-burden-aware Modelling and Interpretation of Multi-site Bone Mineral Density",
  "title_text": "Artificial Scientific Intelligence for Measurement-burden-aware Modelling and Interpretation of Multi-site Bone Mineral Density",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:28Z",
  "url": "https://research.draconitic.com/paper/8pb5yj38qbqa3",
  "venue": "medRxiv",
  "versions": []
}